1. Field of the Invention
The present invention relates generally to enterprise systems management, and in particular, to extracting relevant information based to assist in system administration.
2. Background Information
As businesses rely more and more on technology and computation, the Information Technology (IT) backends supporting these business objectives grow in complexity exponentially. It is becoming common for larger enterprise environments to have peta-byte scale storage with thousands of application servers and complex network interconnectivity in a single data center. Managing such environments is not just challenging due to its scale, but also due to immense heterogeneity of devices co-existing in the environment. As an example, a common administrator frustration is the inability of one storage vendor's management product to not even discover the devices from another vendor, even though they exist in the same network fabric.
As systems management begins to take the dominant share of the overall IT budget, there have been various initiatives to develop standardized mechanisms for reporting device information, for example, the Common Information Model (CIM) from Distributed Management Task Force, Inc. (DMTF) and the Storage Management Initiative Specification (SMI-S) from The Storage Networking Industry Association (SNIA). While products based on such efforts are increasing in popularity, they continue to be highly “data” driven. The objective of such tools is primarily to obtain and report data about attributes and characteristics of the enterprise IT environment.
This is a clear disconnect from the actual administrative objectives which tend to be more “task” driven. Administrators have to perform tasks like resource provisioning, problem determination, performance management, asset usage optimization, backup and disaster recovery. In the current IT environment, accomplishing such tasks requires obtaining data from multiple devices (through management databases and/or unstructured log files), correlating this information with the workloads and usage patterns (performing ad-hoc historical and future what-if analysis) and then developing plans to meet the desired objectives. Obtaining this information often requires complex interplay of multiple graphical user interfaces (GUIs), spreadsheets, post-it markers and back-of-the envelope calculations. As an example, trying to determine the cause of poor performance of a Systems Application Product (SAP) would require analysis of host servers running the application, Host Bus Adapter (HBA) performance impacting the host Input/Output (I/O) throughput, database performance for current workloads, network bandwidth congestion, switch congestion, storage sub-system loads and failures. Such a manual system management style is not only just expertise-intensive, it is also highly unscalable, slow, and error prone.
One approach for alleviating these concerns is to develop numerous monolithic task modules—one each for every conceivable administrative task, such as problem determination, usage optimization, etc. Such an approach is not only hard to implement because it is highly resource intensive and varying with differences in data center environments, it is also very expensive to maintain and cannot be customized enough for each different administrator.